Routing requests to machine learned models

Abstract

Techniques for routing input(s) associated with a machine learned model to various models located at different locations are discussed herein. In some examples, the model may be a generative machine learned model, and in some examples, the different locations may correspond to a first location on a user equipment (UE), a second location in a core network of a network provider, and/or a third location outside of the core network. In some examples, a routing component on the UE may receive an input to a machine learned model and can determine characteristics of the input, and/or characteristics and/or capabilities of the UE, and can route the input to the one or more of the first location, the second location, the third location, or other locations. The UE can receive a response from the location and can present the response at the UE.

What is claimed is: 
     
         1 . A user equipment comprising:
 one or more processors; and   one or more non-transitory computer readable media storing computer executable instructions that, when executed, cause the one or more processors to perform operations comprising:   receiving, at the user equipment (UE), an input associated with a generative machine learned model;   determining a characteristic of the input;   determining a capability of the UE;   determining, based on the characteristic of the input and the capability of the UE, a location to send the input, wherein the location is one of a plurality of locations, wherein the plurality of locations comprises at least a first location associated with a parallel processing unit associated with the UE, a second location associated with a first network node in a core network, and a third location associated with a second network node outside the core network;   sending, based at least in part on the input, data to the location;   receiving, at least partially in response to the data, a response from an instance of the generative machine learned model associated with the location; and   presenting the response at the UE.   
     
     
         2 . The user equipment of  claim 1 , wherein the characteristic of the input comprises at least one of:
 a location preference;   a privacy metric;   an accuracy metric;   an application type;   a latency metric; or   personalized data.   
     
     
         3 . The user equipment of  claim 1 , wherein the capability of the UE indicates whether the UE includes a parallel processing unit configured to host the generative machine learned model. 
     
     
         4 . The user equipment of  claim 1 , wherein:
 the UE is associated with a user profile provided by a wireless communication provider; and   the second location is associated with a private network hosted by the wireless communication provider.   
     
     
         5 . The user equipment of  claim 1 , wherein determining the location to send the input is performed by a machine learned model executing on the UE. 
     
     
         6 . The user equipment of  claim 1 , the operations further comprising;
 sending the input to the first location;   receiving, as a first response, the response from a generative machine learned model executing on the UE;   sending the input to at least one of the second location or the third location;   updating, as an updated response, the first response based at least in part on a second response from the at least one of the second location or the third location; and   presenting the updated response on the UE.   
     
     
         7 . The user equipment of  claim 1 , the operations further comprising:
 sending the input to the second location with an instruction to restrict further sending the input to the third location or another location outside of the core network.   
     
     
         8 . The user equipment of  claim 1 , wherein:
 the first location is associated with a first instance of the generative machine learned model;   the second location is associated with a second instance of the generative machine learned model;   the third location is associated with a third instance of the generative machine learned model; and   the first instance, the second instance, and the third instance are different versions of the generative machine learned model.   
     
     
         9 . A computer-implemented method comprising:
 receiving, at a user equipment (UE), an input associated with a generative machine learned model;   determining a characteristic of the input;   determining a capability of the UE;   determining, based on the characteristic of the input and the capability of the UE, a location to send the input, wherein the location is one of a plurality of locations, wherein the plurality of locations comprises at least a first location associated with a parallel processing unit associated with the UE, a second location associated with a first network node within a core network, and a third location associated with a second network node outside the core network;   sending, based at least in part on the input, data to the location;   receiving, at least partially in response to the data, a response from an instance of the generative machine learned model associated with the location; and   presenting the response at the UE.   
     
     
         10 . The computer-implemented method of  claim 9 , wherein the characteristic of the input comprises at least one of:
 a location preference;   a privacy metric;   an accuracy metric;   an application type;   a latency metric; or   personalized data.   
     
     
         11 . The computer-implemented method of  claim 9 , wherein the capability of the UE indicates whether the UE includes a parallel processing unit configured to host the generative machine learned model. 
     
     
         12 . The computer-implemented method of  claim 9 , wherein:
 the UE is associated with a user profile provided by a wireless communication provider; and   the second location is associated with a private network hosted by the wireless communication provider.   
     
     
         13 . The computer-implemented method of  claim 9 , wherein determining the location to send the input is performed by a machine learned model executing on the UE. 
     
     
         14 . The computer-implemented method of  claim 9 , further comprising;
 sending the input to the first location;   receiving, as a first response, the response from a generative machine learned model executing on the UE;   sending the input to at least one of the second location or the third location;   updating, as an updated response, the first response based at least in part on a second response from the at least one of the second location or the third location; and   presenting the updated response on the UE.   
     
     
         15 . The computer-implemented method of  claim 9 , further comprising:
 sending the input to the second location with an instruction to restrict further sending the input to the third location or another location outside of the core network.   
     
     
         16 . One or more non-transitory computer-readable media storing computer executable instructions that, when executed, cause one or more processors to perform operations comprising:
 receiving, at a user equipment (UE), an input associated with a generative machine learned model;   determining a characteristic of the input;   determining a capability of the UE;   determining, based on the characteristic of the input and the capability of the UE, a location to send the input, wherein the location is one of a plurality of locations, wherein the plurality of locations comprises at least a first location associated with a parallel processing unit associated with the UE, a second location associated with a first network node, and a third location associated with a second network node outside a core network;   sending, based at least in part on the input, data to the location;   receiving, at least partially in response to the data, a response from an instance of the generative machine learned model associated with the location; and   presenting the response at the UE.   
     
     
         17 . The one or more non-transitory computer-readable media of  claim 16 , wherein the characteristic of the input comprises at least one of:
 a location preference;   a privacy metric;   an accuracy metric;   an application type;   a latency metric; or   personalized data.   
     
     
         18 . The one or more non-transitory computer-readable media of  claim 16 , wherein the capability of the UE indicates whether the UE includes a parallel processing unit configured to host the generative machine learned model. 
     
     
         19 . The one or more non-transitory computer-readable media of  claim 16 , wherein:
 the UE is associated with a user profile provided by a wireless communication provider; and   the second location is associated with a private network hosted by the wireless communication provider.   
     
     
         20 . The one or more non-transitory computer-readable media of  claim 16 , wherein determining the location to send the input is performed by a machine learned model executing on the user equipment.